Evaluating percussive performances

ABSTRACT

Measures (for example, methods, systems and computer programs) are provided to evaluate a percussive performance. Percussive performance data captured by one or more sensors is received. The percussive performance data represents one or more impact waveforms of one or more hits on a performance surface. The one or more impact waveforms are analysed. The analysing comprises: (i) identifying one or more characteristics of the one or more impact waveforms; (ii) classifying the one or more hits as one or more percussive hit-types based on the one or more characteristics; and (iii) evaluating the one or more percussive hit-types against performance target data. Performance evaluation data is output based on said evaluating.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to GB Application No. 2011295.9, filedJul. 21, 2020, under 35 U.S.C. § 119(a). Each of the above-referencedpatent applications is incorporated by reference in its entirety.

BACKGROUND OF THE INVENTION Technical Field

The present invention relates to evaluating percussive performances.

Description of the Related Technology

Percussive performers, such as drummers, strive to improve theirperformance accuracy and technique. By evaluating their percussiveperformances, they can identify strengths and weaknesses and, hence,areas for improvement.

US 2013/0247747 A1 relates to an electronic percussion instrument setand musical performance evaluation apparatus. Each of a plurality ofpads is assigned a tone colour of a different musical instrument and hasa surface which a player strikes. A controller having a centralprocessing unit (CPU) identifies a pad in the plurality of pads which isstruck by the player. The controller has a tone generator for generatingmusical tones of a musical instrument assigned to the identified pad.The CPU evaluates the player's performance and scores the performance.More specifically, the CPU awards an amount of points when first andsecond, different pads are struck in a sequence. A display unit fordisplaying the awarded score is also provided.

US 2014/0260916 A1 relates to an electronic percussion device fordetermining separate right- and left-hand actions. The percussion deviceallows a percussionist to learn, through electronic feedback, correctright- or left-hand playing. The percussion device can be struck by thepercussion player on the right or left side of a pad. Alternatively, thepercussion device can connect to a visual detector to detect motions ofthe player's left and right hands. Computer software may display writtenmusic or instructions of which hand the player should play with and atwhat time they should play. The percussion device inputs the performanceof the percussionist into a computer, designating which inputs were fromthe right-hand side and which inputs were from the left-hand side.Optionally, foot sensors can be used to detect movement of the left andright feet to assist in teaching of instruments such as a drum set wherethe feet may control operation of instruments such as a bass drum andhi-hat.

Such electronic systems provide some evaluation of a percussiveperformance.

SUMMARY

According to first embodiments, there is provided a method of evaluatinga percussive performance, the method comprising:

-   -   receiving percussive performance data captured by one or more        sensors, the percussive performance data representing one or        more impact waveforms of one or more hits on a performance        surface;    -   analysing the one or more impact waveforms, wherein the        analysing comprises:    -   identifying one or more characteristics of the one or more        impact waveforms;    -   classifying the one or more hits as one or more percussive        hit-types based on the one or more characteristics; and    -   evaluating the one or more percussive hit-types against        performance target data; and    -   outputting performance evaluation data based on said evaluating.

According to second embodiments, there is provided a system configuredto perform a method according to the first embodiments.

According to third embodiments, there is provided a computer programarranged to perform a method according to the first embodiments.

Further features will become apparent from the following description,given by way of example only, which is made with reference to theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a diagram illustrating an example set of drum rudiments anddrumstick control exercises using notation of the Percussive ArtsSociety International Drum Rudiments;

FIG. 2 shows a schematic representation of an example of a system inwhich percussive performances can be evaluated;

FIG. 3 shows a flow chart illustrating an example of a routine for usein evaluating percussive performances;

FIG. 4 shows a flow chart illustrating another example of a routine foruse in evaluating percussive performances;

FIG. 5 shows a flow chart illustrating another example of a routine foruse in evaluating percussive performances;

FIG. 6 shows a flow chart illustrating another example of a routine foruse in evaluating percussive performances;

FIG. 7 shows an example set of waveform diagrams illustrating exampleimpact waveforms;

FIG. 8 shows a further example set of waveform diagrams illustratingfurther example impact waveforms;

FIG. 9 shows a further example set of waveform diagrams illustratingfurther example impact waveforms;

FIG. 10 shows a further example set of waveform diagrams illustratingfurther example impact waveforms;

FIG. 11 shows a further example set of waveform diagrams illustratingfurther example impact waveforms;

FIG. 12 shows a further example set of waveform diagrams illustratingfurther example impact waveforms;

FIG. 13 shows a further example set of waveform diagrams illustratingfurther example impact waveforms;

FIG. 14 shows a further example set of waveform diagrams illustratingfurther example impact waveforms and shows corresponding frequencyspectra;

FIG. 15 shows a schematic representation of an example of a graphicaluser interface; and

FIG. 16 shows a schematic representation of another example of a systemin which percussive performances can be evaluated.

DETAILED DESCRIPTION OF CERTAIN INVENTIVE EMBODIMENTS

Examples described herein provide more comprehensive evaluation ofpercussive performances than existing systems which, for example, do notperform hit-type classification. Such evaluation may be morecomprehensive in terms of the number of aspects of a performance thatare evaluated and/or the level of detail to which aspects of theperformance are evaluated. Examples described herein evaluate aspects ofperformances that are not, and cannot be, evaluated in existing systems.For example, signal analysis methods described herein may be used to:(i) evaluate relative stick timing of drumstick hits, from a left orright hand, with or without reference to a datum metronome click; (ii)classify ghost notes and/or accent drumstick hits for each hand andevaluate them against a prescribed accented and/or ghost note drumpattern; (iii) measure a contact time of one or more drumstick hits and,hence, a measure of “stick bounce”; (iv) classify and evaluate flam,drag, ruff and buzz drumstick hit types, and/or and any other form ofunique drumstick hit type; (v) calculate one or more user scores, forexample as percentages, and/or one or more other metrics for consistencyand/or accuracy with regards to timing; (vi) calculate one or moreperformance accuracy scores with respect to dynamics and/or hit-typeclassification; and (vii) identify and communicate areas for improvementof performance technique.

Examples described herein provide enhanced functionality with respect tothe Drummer ITP™ software application (release version 1.10, launched on25 May 2020), available from RT60 Ltd. Examples of such enhancedfunctionality include, but are not limited to, (i) classifying one ormore hits as one or more percussive hit-types based on one or moreidentified characteristics of one or more impact waveforms of the one ormore hits, and (ii) evaluating the identified one or morecharacteristics and the one or more percussive hit-types againstperformance target data.

Examples described herein relate to evaluating a percussive performance(which may also be referred to as a “percussion performance”) in whichpercussive performance data represents one or more impact waveforms ofone or more hits on a performance surface. For convenience and brevity,specific examples will be described in which the percussive performanceis a drumming performance, and in which the one or more hits on theperformance surface include one or more drumstick hits caused by adrummer using a pair of drumsticks (which may be referred to simply as“sticks”) on a drumstick practice pad. Other examples of percussiveperformances, hits, performance surfaces, performers and impactingmembers will, however, be described. As such, unless the contextindicates otherwise, specific examples described in the context of adrumming performance should be understood to be applicable to any typeof percussive performance using any type of impacting member on any typeof performance surface.

Drummers regularly use drumstick practice pads (which may be referred tosimply as “practice pads”) along with an audible metronome to practiceand develop their drumstick performance accuracy and technique. Apractice pad is a passive and quiet device that provides an impactresponse and elasticity similar to a tensioned drumhead. Drummers canpractice a number of stick rudiments and exercises with the practicepad. Examples of such rudiments and exercises include, but are notlimited to, single stroke drum rolls, double stroke drum rolls, accentedpatterns, paradiddles, flam patterns and drag patterns. Drummers canalso practice other reversed, inverted, offset and/or bespoke exercises.Such exercises involve stick control, with drumsticks held in both theleft and right hands. Exercise patterns can be practiced with andwithout dynamic accents on certain notes, can be left- orright-hand-led, can be offset to start on different beats of a pattern,and/or can be practiced with different music time signatures and/or atdifferent tempos. Fast tempos involve a different hand technique fromslower tempos.

Referring to FIG. 1, there is shown an example set 100 of drum rudimentsand stick control exercises using notation of the Percussive ArtsSociety International Drum Rudiments. The example set 100 includes asingle stroke roll 101, a single paradiddle 102, a flam paradiddle 103and a single drag tap 104.

Referring to FIG. 2, there is shown an example of a system 200. Theexample system 200 has a combination of hardware and softwarecomponents.

In this specific example, the system 200 is an electronic drum practicesystem 200, which comprises one or more electronic components and whicha drummer can use to practice their drumming.

The example system 200 comprises one or more sensors 201. The one ormore sensors 201 capture percussive performance data. In this specificexample, the one or more sensors 201 comprise a stereo microphone 201.The stereo microphone 201 itself comprises two component microphonesensors, which in this example are left and right microphone sensors.However, in other examples, the system 200 comprises a single sensor 201or comprises more than two sensors 201. In addition, in other examples,the sensor(s) 201 can comprise one or more sensors of a type other thana microphone. For example, the sensor(s) 201 may comprise one or moreaccelerometers, and/or one or more similar sensors. In general, thesensor(s) 201 may comprise one or more transducers.

The example system 200 comprises an electronic control unit (ECU) 202.The example system 200 also comprises a user interface (UI) 203. The UI203 displays system information and facilitates user interaction events.In this example, the ECU 202 manages user interaction events, processessensor data captured by the sensor(s) 201 and provides metrics and/ormetadata related to the measurements described in more detail herein. Inthis example, the ECU 202 also communicates information to the UI 203,which incorporates visual displays. The communication of data betweenthe ECU 202 and UI 203 may be wired or wireless. The communication datamay be configured in a bespoke format or use an established protocol,such as the wired Universal Serial Bus (USB) or wireless Bluetooth™protocols. Such displays may be detailed and/or symbolic. The UI 203 canenable user interaction, performance data displays, feature navigationand/or selection of drumstick practice modes. User interaction via theUI 203 may be via one or more device control buttons, a touch-screeninterface, etc.

The example system 200 has a headphone and loudspeaker output 204. Inparticular, the example system 200 has a headphone output port and abuilt-in loudspeaker and amplifier circuit 204. In some examples, thesystem 200 comprises a wireless data output 204, for example aBluetooth™ data output. However, in other examples, the headphone and/orloudspeaker output 204 may be omitted. In some examples, the system 200does not output audio, such as a metronome reference. In particular,measurement, characteristic identification, classification and/orevaluation may still be conducted even if a metronome click is notactivated.

The example system 200 has an auxiliary audio input 205. The auxiliaryaudio input 205 enables external audio, such as background audio, to beincorporated while practicing. However, in other examples, the auxiliaryaudio input 205 may be omitted.

The example system 200 comprises a performance surface 206 in the formof a practice pad. The practice pad 206 is not physically attached tothe sensor(s) 201.

The example system 200 comprises impacting members 207 in the form of apair of drumsticks. The pair of drumsticks 207 includes left- andright-hand drumsticks 207. The drumsticks 207 are not physicallyattached to other parts of the system 200.

As explained above, the performance surface 206 and/or the impactingmembers 207 may be of different types in other examples.

The example system 200 is configured and arranged to capture data, suchas acoustic data, relating to temporal, dynamic, spatial and/or spectralcharacteristics of one or more drumstick hits of the drumsticks 207 onthe practice pad 206.

Examples will now be described with reference to FIGS. 3 to 5 in whichone or more impact waveforms of one or more hits are analysed. Suchanalysis is referred to herein as “impact waveform analysis”. The impactwaveform analysis enables a percussive performance to be comprehensivelyevaluated. The percussive performance may be a practice performance, alive performance (for example with an audience), or any other type ofperformance.

Three stages of the impact waveform analysis will be described. In afirst stage 300, one or more characteristics of the one or more impactwaveforms are identified. Although examples described below primarilyrelate to the identification of the one or more characteristicscomprising one or more waveform metrics being measured or calculatedbased on the one or more impact waveforms, the one or morecharacteristics may be identified in other ways. For example, the one ormore characteristics may comprise one or more features extracted fromthe one or more impact waveforms, for example using machine learning. Assuch, example characteristics include, but are not limited to, waveformmetrics and features. In addition, identifying the characteristic(s) mayinvolve, but is not limited to involving, measurement, calculationand/or extraction. In a second stage 400, the one or more hits areclassified as one or more percussive hit-types based on the one or morewaveform metrics. In a third stage 500, the one or more waveform metricsand the one or more percussive hit-types are evaluated againstperformance target data.

In these examples, the impact waveform analysis is implemented in theexample system 200 described above with reference to FIG. 2. Forexample, the impact waveform analysis may be implemented by the ECU 202.

Referring to FIG. 3, the first stage 300 is shown.

At item 301, percussive performance data is captured by the sensor(s)201 and serves as an input to the first stage 300. Percussiveperformance data may be referred to as “sensor data”, “signal data”,“waveform data” or the like. The percussive performance data representsone or more impact waveforms of the hit(s) on the performance surface206. The impact waveform(s) represent one or more profiles of thepercussive performance. Where the sensor(s) 201 comprises a stereomicrophone, the percussive performance data may represent one or moreacoustic profiles (which may be referred to as “acousticcharacteristics”) of the hit(s).

At item 302, an analogue-to-digital convertor (ADC) circuit converts thepercussive performance data from analogue to digital form. Theanalogue-to-digital conversion performed by the ADC circuit is driven bya digital clock 303. The digital clock 303 may be referred to as a“digital system clock”, a “signal clock”, or the like. The digital clock303 is a timing unit which enables capture, processing, computationand/or control of system data at regular, timed intervals. The digitalclock 303 is responsible for enabling accurate sampling of thepercussive performance data at a specified sample rate. The specifiedsample rate may be 48 kHz, for example. The digital clock 303 enablescalculation of time-based waveform metrics to an accuracy of one audiosample.

In this example, at item 304, following the conversion of the percussiveperformance data into digital form at item 302, signal pre-processing isemployed. Such signal pre-processing may use a number of establishedaudio processing techniques. Examples of such audio processingtechniques include, but are not limited to, low-pass filtering,high-pass filtering, dynamic range compression and noise removal.

At item 305, the first stage 300 responds to the input percussiveperformance data by identifying a percussive event (which may also bereferred to as a “percussive performance event”). The percussive eventmay correspond to a drummer hitting the practice pad 206 with adrumstick 207. In this example, the percussive event is identified whenthe signal amplitude in the digital-form percussive performance dataexceeds a threshold value. The threshold value can be calibrated invarious ways. The system 200 may be calibrated to identify a single(also referred to as a “momentary”) threshold exceedance. The system 200may be calibrated to identify the percussive event only when aconfigured number of samples consecutively exceeds the threshold value.The system 200 may be calibrated to identify the percussive event onlywhen a signal average exceeds a threshold value. Incorporating a numberof different threshold exceedance techniques enables thewaveform-capture routine, which includes items 302 to 305, to beresponsive to an accuracy of one audio sample accuracy, whilst alsobeing reliable and robust. The waveform-capture routine is reliable inthat false positives owing to background noise can be ignored. Thewaveform-capture routine is robust in that double-triggers from a singleimpact can be avoided.

As indicated by item 306, when no threshold signal exceedance isidentified at item 305, the first stage 300 does not perform any furtherclassification-based analysis of the percussive performance data.

At item 307, a threshold exceedance event is identified. A calibratedarray (which may also be referred to as a “window”) of consecutivesample data is captured. The calibrated array is stored as an impactwaveform. The impact waveform describes the dynamic profile of thepercussive event. The size of the window (which may also be referred toas the “window size”) and, hence, the number of samples of waveform datacaptured per percussive event, is determined by a number of variables.Examples of such variables include, but are not limited to, the clockspeed of the clock 303, the sample rate used during analogue-to-digitalconversion at 302, the tempo of the percussive performance and one ormore calibration values. The tempo may be expressed in terms ofbeats-per-minute (BPM). As will be described in more detail below, thecalibration value(s) may relate to the type of percussive performance,the type of the impacting member 207, the type of performance surface206, and/or any other performance context variable. Enabling the windowsize to be variable enables the impact waveform analysis conducted bythe system 200 to be accurate in relation to the specific performancecontext and the expectant waveform profiles of different performancesurfaces 206, impacting members 207, and/or performance tempos.

Once an impact waveform has been captured, one or more waveform metriccalculations are performed at item 308.

The waveform metric calculations are performed to classify and evaluatethe percussive performance data.

In this example, at item 309, one or more temporal metrics arecalculated. Temporal metrics are the results of signal processingcalculations related to the time of a hit occurrence and/or the durationof a hit. Temporal metrics enable the system 200 to evaluate theperformer's accuracy and/or technique. For example, temporal metrics canenable the system 200 to analyse the performer's performance against areference metronome sound. Temporal metrics can also enable the system200 to evaluate timing consistency, for example of a drummer's left-handhits. Temporal metrics include, but are not limited to, threshold exceedtime, waveform peak time, threshold recede time and sustain time. Thewaveform peak time is the measured time of a peak value within an impactwaveform. The threshold recede time is the time of an event where themeasured impact waveform amplitude, having previously exceeded athreshold, falls back below the threshold. The sustain time is theduration of which an amplitude threshold exceedance continually repeatswithin a captured impact waveform, before falling permanently below thethreshold value.

In this example, at item 310, one or more dynamic metrics arecalculated. Dynamic metrics are the results of signal processingcalculations related to impact waveform amplitude and rate of change ofimpact waveform amplitude. Dynamic metrics also enable the system 200 toevaluate the performer's accuracy and/or technique. Dynamic metrics can,for example, enable the system 200 to classify a drumstick hit as an“accented” (in other words, a purposefully loud) hit. Dynamic metricscan enable the system 200 to evaluate dynamic consistency, for exampleof a drummer's right-hand hits. Dynamic metrics include, but are notlimited to, peak amplitude, average amplitude, attack gradient, anddecay gradient. The peak amplitude is the greatest absolute amplitudevalue within an impact waveform. The average amplitude is the averageamplitude value of an impact waveform and can be calculated by averagingsome or all of the impact waveform values. The averaging calculation maybe based on a root-mean-square (RMS) or may use a similar audio signalaveraging calculation. The attack gradient is the rate of change ofabsolute or average amplitude of an impact waveform prior to a peakamplitude being reached. The decay gradient is the rate of change ofabsolute or average amplitude of an impact waveform after a peakamplitude has been reached.

In this example, at item 311, the impact waveform is converted intoassociated frequency domain data. In this example, such conversionimplements a fast Fourier transform. However, other conversiontechniques could be used in other examples.

In this example, at item 312, one or more spectral metrics arecalculated. Spectral metrics also enable the system 200 to evaluate theperformer's accuracy and/or technique. Spectral metrics may enable thesystem 200 to identify sonic characteristics that may be differentbetween left- and right-hand hits. Hence, spectral metrics can give anindication of different performance techniques used by each hand.Spectral metrics include, but are not limited to, identification ofdominant frequencies, spectral density, frequency peak width, spectralcentroid, spectral spread and harmonic strength. Dominant frequenciesare frequency values associated with peaks identified on the frequencyspectrum of the impact waveform. Spectral density is the abundance offrequency peaks and frequency content on the frequency spectrum of theimpact waveform. Spectral density may be calculated in various ways.Example techniques for calculating spectral density include, but are notlimited to, measuring the area under the curve of the frequencyspectrum, and mathematical integration of the frequency spectrum.Frequency peak width is the width of dominant frequency peaks, measuredat a specified relative power amplitude on the frequency spectrum.Spectral centroid is the frequency at the centre of the spectral powerdistribution. Spectral spread is calculated as a measure of thedistribution of the spectrum from the spectral centroid. Harmonicstrength is the spectral density of harmonics of one or more dominantfrequencies. Harmonic frequencies are defined as occurring at integermultiples of a specified dominant frequency.

Collectively, the measured and calculated temporal metric(s), dynamicmetric(s) and/or spectral metric(s) constitute impact waveform metricdata 313. The impact waveform metric data 313 is used in the second andthird stages 400, 500. As such, the impact waveform metric data 313 isused for impact waveform analysis and, in particular, classification andevaluation.

Referring to FIG. 4, the second stage 400 is shown.

In the second stage 400, the impact waveform metric data 313 resultingfrom the first stage 300 is analysed with reference to calibration data401 to implement a hit-type classification routine 402. The calibrationdata 401 may comprise programmed and/or machine learnt calibration data.The hit-type classification routine 402 is a digital routine forclassifying one or more hits as one or more percussive hit-types basedon the one or more impact waveforms that represent the one or more hits.The term “hit-type” describes a typology of one or more hits on theperformance surface 206. In the context of a drumming performance,example hit-types include, but are not limited to, single strokes,double strokes, flam strokes, drag strokes, ruff strokes, buzz strokes,buzz rolls, rim shots, and combinations thereof. Hit-type classificationenables the system 200 to evaluate the percussive performance against aperformance target. Such evaluation can, for example, verify howaccurately a drummer delivered an accented paradiddle performance. Suchaccuracy may be in terms of performing each correct hit-type at theright time, and/or performing each hit accurately and consistently withrespect to each hit, and/or with reference to a target metronome click.Temporal calculations may still be performed and used in the absence ofa metronome click or reference tempo. For example, a single drag tap hastiming attributes within its performance (ghost-ghost-tap-accent),regardless of whether it is in time with respect to a reference click ornot.

In this example, the hit-type classification routine 402 incorporates anumber of classification sub-routines.

In this example, at item 403, dynamic classification is performed.Dynamic classification 403 involves analysis and classification of thedynamic properties of an impact waveform. Dynamic properties are basedon amplitude against time. Dynamic classification 403 may use a numberof amplitude values to classify the impact waveform. The amplitudevalues may be absolute and/or averaged. The impact waveform may, forexample, be classified as soft, normal or loud. In the context ofdrumstick hits, the impact waveforms may be classified as ghost notes,grace notes, standard notes and accented notes. Ghost notes and gracenotes are similar to each other. A ghost note is a quiet note in its ownright, and a grace note is a note that leads another note and is usuallyquiet. In examples, grace note analysis therefore involves temporalanalysis and feedback classification. Dynamic classification 403 mayincorporate analysis of spectral data, since power and contact timeimpacts excite different vibration frequencies in performance surfaces206 and impacting members 207.

In this example, at item 404, positional classification is performed.Positional classification 404 incorporates classification of an impactwaveform based on the positioning of the percussive performance. Thepositioning may be spatial or locational. Spatial and locationalpositioning are similar to each other. However, as used herein, spatialpositioning is measured by acoustic transmission and relates to aposition in a fluid (such as air), and locational positioning ismeasured by vibration and relates to a position on a solid (such as on aplate or surface). In the context of drumstick hits, positionalclassification 404 may involve comparing impact waveform amplitudes fromdifferent channels of a multichannel signal. For example, positionalclassification 404 may evaluate whether the left channel data has agreater amplitude than the right channel data. If so, the impactwaveform can be associated with, or denotes, a left-hand hit. One ormore spectral metrics may be used to assist with positionalclassification 404. For example, different locations of impact mayexcite uniquely different spectral profiles. The role of positionalclassification 404 with respect to drumstick hits is to classify whichdrumstick hits were performed by which drumstick held in which of thedrummer's hands. Positional classification 404 is therefore implementedto identify the hand (left or right) which was responsible for aparticular impact and/or combination of impacts. Positionalclassification 404 may be extended for more detailed classification ofthe drumstick hit position in three-dimensional space. For example, thedrumstick hit position may be classified as high, low, close, far, left,right, centre, etc. Left- and right-hand drumstick impacts may beindependently detected.

In this example, at item 405, hit-type classification 405 is performed.Hit-type classification 405 uses the results of the dynamicclassification 403 and/or the positional classification 404 to classifyhits and/or combinations of hits. Hit-type classification 405 may useprogrammed benchmarks and/or classification features and parameters frommachine learnt impact waveform profiles. For example, a single hit maybe classified as a quiet (also known as a “ghost”) right-hand drumstickhit. However, by additional analysis of one or more prior and/orfollowing drumstick hits, the drumstick hit may also be classified asbeing part of a predetermined sequence of drumstick hits. As such, thedrumstick hit may be classified as being the first drumstick hit withina recognised drumstick stroke that incorporates more than one drumstickhit. Knowledge of the prior and/or following drumstick hits may besequentially held and/or fed back, as indicated at item 406, into thehit-type classification 405. For example, a quiet right-hand drumstickhit followed quickly by an accented left-hand drumstick hit describes aleft-hand flam stroke. The flam stroke is a drum stroke whichincorporates two drumstick hits played in a particular way. Differentdrumstick hit types performed with either the left or right hand cantherefore be classified.

Collectively the dynamic, positional and/or hit-type classificationresults constitute combined hit-type classification data 407.

Referring to FIG. 5, the third stage 500 is shown.

In this example, at item 501, a performance evaluation sub-routinecompares the impact waveform metric data 313 from the first stage 300and the hit-type classification data 407 from the second stage 400against performance target data 502. The performance evaluationalgorithm 501 calculates the objective achievement of accuracy targetsand provides further metrics related to performance technique. Theperformance target data 502 can be used by the performer as a referencewhen delivering a percussive performance. The performance target data502 may include, but is not limited to including, a reference metronometiming signal, a notated percussion pattern, a rudiment technique orpractice exercise to be followed, historic results for comparisonagainst, and benchmark results of other performers for comparisonagainst. The reference metronome timing signal may be fixed or variablewith respect to tempo, volume, dynamic profile and/or spectral profile.The reference metronome timing signal may comprise a computer-controlledclick sound, which may maintain a consistent or programmed profile.

In this example, at item 503, an accuracy evaluation sub-routineevaluates the impact waveform metric data 313 and the hit-typeclassification data 407. Different accuracy evaluation criteria may beprogrammed into the accuracy evaluation routine 503.

One example accuracy evaluation criterion is hit accuracy. Hit accuracyis a measure of how successfully the performer enacted one or moredesignated strokes, hits, patterns and/or sequences of hits, for exampleas directed by a target exercise. The target exercise may be notated orotherwise communicated to the performer. In the context of a drumstickperformance, stroke accuracy may give a measure of the successfulperformance of one or more specific drum strokes in a sequence asspecified by the performance target data 502. The performance targetdata 502 may be in the form of, for example, a notated drumstick patternor described practice exercise. This can enable a drummer's performanceaccuracy with respect to a chosen rudiment exercise and/or stick patternto be evaluated.

Another example accuracy evaluation criterion is hit quality. Hitquality is a measure of the temporal, dynamic and/or spectral qualitiesof a particular classified hit-type with respect to a benchmark (alsoreferred to as “exemplar”) profile of the classified hit-type. In thecontext of a drumstick performance, hit quality denotes a measure of thesimilarity between a particular drum stroke and a benchmark idealequivalent. For example, the benchmark ideal may identify the temporaland/or dynamic qualities of a drumstick hit that has been classified asa drag stroke.

Another example accuracy evaluation criterion is timing accuracy. Timingaccuracy is the timing of a percussive hit or hits in comparison to areference, such as a reference metronome or timing chart. Timingaccuracy may be calculated by measuring the timing difference betweenthe absolute time of a metronome click event and the absolute time of aclassified percussive hit. Timing accuracy may be calculated in variousways. For example, timing accuracy may be calculated with respect to thetime an impact waveform threshold is exceeded, the time of an impactwaveform peak, and/or the time an average impact waveform amplitudeexceeds a threshold value. Timing accuracy may be measured in samples,milliseconds, as an error value based on the intended performance tempo,and/or in another manner. The error value may, for example, correspondto an accuracy and/or inaccuracy percentage related to one semiquavertime period. Using a number of timing accuracy calculations enables morereliable results to be gathered for a specific performance context. Forexample, some performance surfaces 206 may be hard and have a rapidimpact waveform attack leading to a very clear peak value calculation,whereas other performance surfaces 206 may be soft and, hence, have aless apparent single impact waveform peak. An average amplitude analysistechnique may, therefore, assist with accurately and reliablyidentifying the timing and, hence, the accuracy of the impactoccurrence. In scenarios in which the performer purposefully wishes topractice playing a specified time period ahead of or behind thereference beat, then timing accuracy evaluations can be adjusted (alsoreferred to as “offset”) to give a value related to their specificperformance intention. The precise impact time of each hit can therebybe measured, which can enable a performer's temporal performanceaccuracy to be evaluated, for example with respect to an acoustic and/orvisual datum metronome signal.

Another example accuracy evaluation criterion is timing consistency.Timing consistency is a metric related to the variance or standarddeviation of timing accuracy. This can be an effective measure because aperformance may be consistently inaccurate. For example, the performancemay be consistently 20 milliseconds late, behind the metronome datum.This differs considerably from a performance that repeatedly fallsbehind or rushes ahead of the reference metronome click. Some percussiveperformers may accidentally or purposefully play “behind the beat” or“ahead of the beat” for certain music genres. In such instances,evaluation of performance consistency can be more valuable to theperformer than performance accuracy. Temporal accuracy values can alsobe calculated without comparison to a reference metronome click, insteadbeing calculated with reference to the timing of other strokes or hitswithin the performance pattern. For example, a “drag-tap” patternfeatures a repetition of two semi-quaver notes followed by two quavernotes. Temporal accuracy evaluation of a “drag-tap” pattern cantherefore be, for example, a measure of accuracy that the semiquavernotes are always at double length time intervals with respect to thesemi-quaver notes, regardless of the performance tempo. Temporalconsistency evaluations related to the relative timing of strokes orhits can therefore also be conducted.

Another example accuracy evaluation criterion is dynamic accuracy.Dynamic accuracy is the dynamic classification of one or more hits whencompared to one or more dynamic profiles denoted in a percussiveperformance sequence as specified by the performance target data 502,for example in the form of a notated drumstick pattern or a describedpractice exercise. In the context of a drumstick performance, dynamicaccuracy can give a measure that denotes how often a soft or accentedhit-type was performed at the correct moment, for example as designatedby a reference performance exercise that includes grace, ghost and/oraccented notes.

Another example accuracy evaluation criterion is dynamic consistency.Dynamic consistency is a measure of the variance or standard deviationof one or more dynamic metrics for a number of classified hit-types.This can be an effective measure for a number of scenarios. A dynamicconsistency metric can be used to verify whether both left and righthands are performing with similar dynamic characteristics. Dynamicconsistency can be used to verify that all accented notes are of similardynamic strength. A corresponding verification can be performed forghost notes and standard notes, for example. Dynamic consistency canalso give an overall measure of dynamic consistency related to multipledrum roll strokes and/or other performance patterns.

In this example, alongside the accuracy evaluation routine 503 is atechnique evaluation routine 504. The technique evaluation routine 504calculates one or more metrics relating to performance technique. Thetechnique evaluation routine 504 advises the performer on methods forimproving performance technique.

Various example technique evaluation metrics will now be described, byway of example only.

One example technique evaluation metric is left-/right-hand consistencyand/or accuracy evaluation. This is a niche or holistic evaluation ofthe percussive performance with respect to the performer's controland/or accuracy between the left and right hands. In the context ofdrumstick hits, this evaluation metric can evaluate the consistency andvariance of some or all metrics and evaluation data with respect to thehand which performed or led (also referred to as “instigated”) one ormore drumstick hits. For example, this evaluation metric may identifywhether either hand generates impact data that has dynamic and/orspectral differences compared to the other hand. This evaluation metricmay also identify whether one hand is more likely responsiblespecifically for timing inaccuracies on a two-hand percussiveperformance. For example, if the left hand is consistently inaccuratewith timing but the right hand is consistently accurate, the techniqueevaluation algorithm 504 can highlight this and can suggest practiceexercises specifically aimed at improving the left-hand timing accuracy.

Another example technique evaluation metric is stick bounce. Stickbounce (which may be referred to as “impulse contact time”) is a measurecorrelated to the contact time between a drumstick 207 and a performancesurface 206 during a hit event. Hence, stick bounce gives an indicationof the performer's technique in bouncing the drumsticks 207 in theirfingers versus driving the drumstick 207 into the performance surface206 with the drumsticks held more rigidly in the fingers and a hitmotion controlled predominantly with their wrists. If the drumsticks 207generate too much contact time with the performance surface 206, thesystem 200 may suggest modifications and/or practice exercises to theperformer to improve their stick bounce when hitting. Generally, atechnique with more bounce (a shorter surface contact time) is a moreefficient performance technique at higher performance speeds, enablingaccuracy to be maintained at higher tempos and minimising the potentialfor performer fatigue and injury. As such, the impulse contact time ofeach drumstick hit can be measured. This enables a metric to bedetermined which allows drummers to evaluate how much they allow thedrumstick 207 to freely bounce back off the performance surface 206 incomparison to a more rigid drumstick technique that drives the drumstickfirmer into the performance surface 206 for a longer period of time.Spectral analysis may also be particularly effective for stick bounceclassification, since a more rapid impulse (in other words, a shortercontact time) allows the performance surface 206 to vibrate with lessloading and, hence, more freely and for longer.

Another example technique evaluation metric is speed limitation. Speedlimitation is the identification of one or more performance tempos whichappear to challenge a particular performer the most. Drummers often findit challenging to play very slow or very fast and an indication of wheretheir performance drops off at these extremes is valuable to know.Additionally, many performers find there is a challenging tempo range inthe middle of their overall performance range where stick techniquechanges from a more rigid wrist technique to bouncing the drumsticks inthe fingers. Identification of this speed limitation is valuable to theperformer, as is tracking speed limitations for variances andimprovements over time.

Another example technique evaluation metric is spectral consistency.Spectral consistency involves analysis of the frequency spectrum fordifferent drumstick hits. Spectral consistency can give an indication ofsonic differences between, for example, the left and right hand. Aperformer may be able to play accurately and consistently, yet theirtechnique causes drumstick hits in each hand to sound different.Spectral analysis of each hand's drumstick hits can be used to identifyone or more acoustic characteristics which may indicate inconstantperformance techniques between the two hands.

The dynamic power of each drumstick hit can be measured. This can enablequiet (“ghost”) notes, standard-volume notes and louder, accented notesto be identified. As such, notes of varying degrees of “loudness”classification can be identified and classified. Performance accuracyagainst a chosen rudiment exercise and/or stick pattern, incorporatingaccented and/or ghosted notes, can also be evaluated. Dynamic analysisof each drumstick hit also allows the performance consistency ofloudness and the overall dynamic profile of each drumstick hit to beevaluated with respect to a classification of each dynamic hit for eachhand, and/or in comparison between the hands. This can enable, forexample, a measure of the consistency of loudness in all accenteddrumstick hits on the right hand to be determined, and/or theconsistency of accented drumstick hits between the left and right handsto be evaluated.

In this example, resultant technique evaluation data 505 and accuracyevaluation data 506, along with the waveform metric data 313, hit-typeclassification data 407 and the performance target data 502 areforwarded to an output system 507. As such, performance evaluation datais output based on the evaluating carried out in the third stage 500. Inthis example, the performance evaluation data comprises both thetechnique evaluation data 505 and the accuracy evaluation data 506, butcould comprise other and/or additional data in other examples.

The output system 507 can incorporate a number of features.

One example feature of the output system 507 is the UI 203. The UI 203enables data to be communicated to the performer. The UI 203 alsoenables user input to be received before, during and/or after theperformances take place. The UI 203 may take the form of a touch-screeninterface, such as a high-resolution touch-screen interface. The UI 203may be designed as a number of physical control buttons, for examplewith bespoke designed displays and/or indicators. The user may beinformed of impact waveform metric data 313, hit-type classificationdata 407, technique evaluation data 505 and/or accuracy evaluation data506 in near real-time, via the UI 203, while they are performing. Thisallows the performer to positively modify their performance as theyperform, with guided feedback. The use of real-time feedback to theperformer enables them to identify the quantitative results of smalltechnique changes made while they perform. This results in a rapidlearning feedback loop that is capable of accelerating a percussionperformer's development. In some examples, intelligent performanceanalysis routines that intelligently analyse performance traits providetailored learning guidance to the user via the UI 203, for example whilethe performer is performing. Bespoke guidance may be provided to theperformer via the UI 203, for example when a specific weakness in theirperformance is identified. The UI 203 may also or instead display thecorresponding musical notation of the hit(s) classified and evaluatedduring the performance. The notation of a performance is valuable forenabling the user to compare their performance visually against anytarget performance data (such as notation) which was used during theperformance. Displaying the performance evaluation data as musicnotation also provides a useful visual tool for automaticallytranscribing or documenting a percussive performance, allowing timesavings in creating music notation of drum-stick or other percussionpatterns.

Another example feature of the output system 507 is a database storagefeature 508. The database storage feature 508 enables users to storetarget performance data 502 and/or percussive performance data. As such,the database storage feature 508 may allow functionality including, butnot limited to, tracking of performance metrics over time, historicalanalysis of performance achievements, bespoke exercises and percussiveperformance targets to be set and/or recorded. The performer may be ableto design their own practice patterns and store them using the databasestorage feature 508.

Another example feature of the output system 507 is a network sharingfeature 509. The network sharing feature 509 can enable both visibilityof, and access to, output data between users. The network sharingfeature 509 facilitates network-connected and/or online percussiveperformance charts, comparisons and/or competitions. As such, onlineand/or network connectivity can be incorporated, allowing users to sharepractice patterns and/or performance results with other users. Suchsharing may be enabled through an online collaboration database. Assuch, users can access predefined drumstick rudiments and/or practicepatterns and/or can design their own bespoke practice patterns.

The impact waveform analysis described herein enables a percussiveperformance to be evaluated comprehensively. Many parameters of thepercussive performance can be evaluated and fed back to the performer,for example during a percussive performance. Such a comprehensiveevaluation enables the performer to identify specific techniques topractice and allows the performer to fine-tune the techniques to achievea desired level of competency.

Referring to FIG. 6, there is shown an example of a calibration routine600. In some examples, machine learning is used to improve measurementand/or classification accuracy with respect to system latency of thesystem 200, the performer's performance techniques, and/or differenttypes of performance surfaces 206.

As explained above, the impact waveform analysis uses calibration data401 to enable the system 200 to perform to a high-quality standard undera number of different scenarios. The calibration data 410 may compriseprogrammed and/or machine learnt calibration data. Calibration enablesthe system 200 to optimise for different circumstances and/or setupscenarios and/or performance contexts. For example, the system 200 canbe optimised for percussive performances with different types of impactmembers 207 and/or for different performance surfaces 206.

Calibration can be conducted during manufacture of the system 200 and/orin-field. For example, impact threshold values for triggering a waveformcapture event may be factory-set. However, the user may be able tomodify the sensitivity of the impact waveform analysis through thesystem UI 203. Similarly, pre-programmed parameters for classifyingdifferent hit-types may be incorporated into the manufacture. However, amachine learning routine may be used to further improve theclassification algorithm in-field by the user.

The calibration routine 600 may incorporate various differentcalibration sub-routines.

One example calibration sub-routine is a latency calibration sub-routine601, which may be referred to as a “latency differential” sub-routine.The latency calibration sub-routine 601 calculates the digitalprocessing time difference between an output signal 602 being output anda captured input 603 of the same signal. The output signal 602 maycomprise a metronome click sound. The output signal 602 may be outputthrough one or more loudspeakers 204. The captured input 603 of the samesignal may be the same metronome click. The input 603 may be capturedthrough the sensor(s) 201. The latency differential 604 and, hence, thelatency calibration data 605 output by the latency calibrationsub-routine 601 may be used to make temporal measurements accurate toone audio sample. The latency calibration data 605 may incorporatelatency average values and/or latency values for different features ofthe system 200. The latency calibration data 605 may enable dataprovided to the user via the UI 203 to be immediate, in other words inreal-time. For example, a user may choose to use Bluetooth™ (and/orother wireless-enabled) headphones to listen to a metronome click trackthey are using as a performance target. Implementing the latencycalibration sub-routine 601 on the system 200 can, hence, eliminatetemporal metric inaccuracies that might be caused by inherent timedelays associated with Bluetooth™ audio transfer.

Furthermore, the latency calibration sub-routine 601 may involvemultiple latency calculations, for example where two-way latency isencountered in the system 200. A particular example is if wireless, suchas Bluetooth™, communications are used for both transmitting themetronome click sound and for communicating performance data from thedevice ECU 202 to the UI 203. In this instance, bi-directional latencyis encountered and the calibration accounts for both the latencyassociated with the transmitted metronome sound and the latencyassociated with the transmitting of performance data. In this scenario,the latency calibration sub-routine enables two-way latency compensationto be implemented in order to realise accurate and real-time performancedata analysis.

Another example calibration sub-routine is a dynamics calibrationsub-routine 606. The dynamics calibration sub-routine 606 usespre-programed parameters. The dynamics calibration sub-routine 606incorporates gathering example impact data to enhance the dynamicsclassification accuracy of the system 200. For example, a user mayfollow a calibration routine to give examples of multiple ghost,standard and accent notes at item 607. The system 200 can use suchexample hits to identify optimal upper and/or lower thresholds forclassification between the different dynamic types at item 608. Theupper and/or lower thresholds can be stored as dynamics calibration data609. The dynamics calibration data 609 may comprise additional dynamicscalibration values.

Another example calibration sub-routine is a hit-type calibrationsub-routine 610. Hit-type classification accuracy may be enhanced bymachine learning as part of the hit-type calibration sub-routine 610. Atitem 611, a user is prompted to give multiple example hits of alldesignated hit-types. At item 612, a machine learning routine isimplemented to identify one or more classification features orparameters that best identify and classify each unique hit-type.Hit-type calibration data 613 may incorporate positional data to improveclassification accuracy with respect to left-hand and right-hand, and/orother positional classifications. The one or more machine learntclassification features or parameters make up the hit-type calibrationdata 613. The hit-type calibration data 613 may include pre-programmedand/or user-adjusted settings. As such, machine learning may be used toimprove classification of different drumstick hit-types.

Collectively, the calibration data relating to latency, dynamics and/orhit-type constitute the calibration data 401.

As such, the example system 200 can incorporate a learning calibrationfeature. The learning calibration feature can enable the example system200 to calibrate for real-time measurement latency compensation, tointelligently differentiate between different types of drumstick hitsand/or impacting members 207 and/or to calibrate for differentperformance surfaces 206. Machine learning can be used to improveclassification of different drumstick hits and can improvepersonalisation of analysis of a performance. Such personalisation canallow for different users' drumming techniques.

Examples have therefore been described in which a hardware- andsoftware-based percussion practice classification system 200 isprovided. The system 200 can analyse drumstick hits on a performancesurface 206. The system 200 may incorporate a stereo microphone 201(which may be referred to as a “spatial microphone”), which includes twoor more independent microphone capsules. The stereo microphone 201 cangather detailed acoustic information relating to the drumstick hit(s).Using two or more microphones sensors 201 enables location informationabout the drumstick hits to be extracted from recorded acoustic dataand, hence, enables each hit to be classified as originating from theleft- or right-hand drumstick 207. The stereo microphone 201 may includeleft- and right-facing (also referred to as “left- andright-positioned”) microphone capsules and, hence, can pick up acousticmeasurements that incorporate positional information about the soundsource. Additionally, the acoustic impulse data captured by the stereomicrophone 201 gives detailed temporal information about the drumstickhit(s). The temporal information is related to the precise timing anddynamic profile of each drumstick hit, where the dynamic profilecorresponds to volume, power, or amplitude as a function of time. Wherevaluable for classification and/or calibration, frequency spectra fordrumstick hits can also be calculated from recorded microphone data.This may involve a standard or fast Fourier transform, for example.Evaluating the spatial, temporal, dynamic and/or spectral profiles ofthe acoustic data enables drumstick impact measurements on a standarddrumstick practice pad 206 to be gathered, classified and/or evaluated.

Referring to FIG. 7, there is shown a set of three example impactwaveforms 700.

In this example, all the hits represented in the impact waveforms 700are right-hand hits and are measured by the right-side microphone of thestereo microphone 201. The example impact waveforms 700 highlight thedifferences between ghost notes, standard notes and accented notes.

Impact amplitude correlates with the loudness of different drumstickhits on a single hand. Example amplitude thresholds are shown toindicate how an example, low-complexity classification protocol can beused to classify the hits. In this example, an impact detectionthreshold is set at ±0.1, a lower dynamic threshold is set at ±0.25 andan upper dynamic threshold is set at ±0.75. Waveform peak values may beused to identify which amplitude thresholds are breached and, hence, toenable dynamic classification of the hits. Average signal values may becalculated and evaluated against average threshold values to assist withdynamic classification.

The top impact waveform 701 represents an example ghost drumstick hitand is classified as a ghost note since it has passed the impactdetection threshold, but has not passed the lower dynamic threshold.

The middle impact waveform 702 represents an example standard drumstickhit and is classified as a standard note since it has passed the impactdetection threshold and the lower dynamic threshold, but has not passedthe upper dynamic threshold.

The bottom impact waveform 703 represents an example accented drumstickhit and is classified as an accented note since it has passed the impactdetection threshold, the lower dynamic threshold, and the upper dynamicthreshold.

Referring to FIGS. 8 and 9, there are shown further sets of exampleimpact waveforms 800, 900.

The example impact waveforms 800 and 900 represent example stereo sensorsignals captured for the left hand and right hand respectively. As such,the example waveforms 800 and 900 illustrate classification of left- andright-hand drumstick hits respectively. A left-hand drumstick hit ismeasured more strongly by the left-side microphone sensor, andvice-versa for the right-hand side. The differentiation between left-and right-hand hits is, in this example, by the absolute amplitude ofthe peak value of each impact, denoted a_(L) and a_(R) respectively. Inparticular, impact waveform 801 (captured by the left microphone sensor)has a peak amplitude value of a_(L)=0.79, which is greater than the peakamplitude value of a_(R)=0.27 of impact waveform 802 (captured by theright microphone sensor). As such, the hit represented by impactwaveforms 801 and 802 is classified as a left-hand hit. In contrast,impact waveform 901 (captured by the left microphone sensor) has a peakamplitude value of a_(L)=0.35, which is lower than the peak amplitudevalue of a_(R)=0.282 of impact waveform 902 (captured by the rightmicrophone sensor). As such, the hit represented by impact waveforms 901and 902 is classified as a right-hand hit. Analysis of the averagewaveform amplitude can be used to represent the relative signal powerfrom each hand, and can be used to assist with classification. Moredetailed analysis of left- and right-hand microphone data allows theidentification of strokes that are intended to be played with both handsat the same time, enabling accuracy metrics relating to thesynchronicity of such two-hand hits.

Referring to FIGS. 10 and 11, there are shown further sets of exampleimpact waveforms 1000, 1100. The example impact waveforms 1100, 1100show how hits can be classified as flam and drag hits respectively.

Impact waveforms 1000 represent stereo signal waveforms andclassification of a flam drumstick hit. A flam hit is identified by asoft left-hand (grace) hit followed by an accented right-hand hit or asoft right-hand (grace) hit followed by an accented left-hand hit. Inparticular, impact waveform 1001 (captured by the left microphonesensor) represents two hits, with the first hit being classified as aleft-hand grace note. The impact waveform 1002 (captured by the rightmicrophone sensor) also represents two hits, with the second hit beingclassified as a right-hand accent note. In addition, impact waveforms1001 and 1002 are collectively classified as a flam drumstick hit. Assuch, examples enable measurement, classification and timing of flamhits, where one drumstick is played quietly with one hand and followedquickly by a louder, accented drumstick hit with the following hand.This enables a drummer's performance accuracy with respect to a chosenrudiment exercise and/or stick pattern incorporating flam hits to beevaluated.

Impact waveforms 1100 represent example stereo signal waveforms andclassification of a drag drumstick hit. The drag is identified by twoleft-hand grace hits followed by an accented right-hand hit or tworight-hand grace hits followed by an accented left-hand hit. Inparticular, impact waveform 1101 (captured by the left microphonesensor) represents three hits, with the first and second hits beingclassified as a left-hand double grace note. The impact waveform 1102(captured by the right microphone sensor) also represents three hits,with the third hit being classified a right-hand accent note. Inaddition, impact waveforms 1101 and 1102 are collectively classified asa drag drumstick hit. As such, examples enable measurement,classification and timing of drag and/or ruff hits, where drumstick hitsare played as two or more hits at twice (or another multiple of) thespeed of the performance context or musical tempo. This enables adrummer's performance accuracy with respect to a chosen rudimentexercise and/or stick pattern incorporating drag and/or ruff hits to beevaluated.

Referring to FIG. 12, there is shown a further set of example impactwaveforms 1200.

Impact waveforms 1200 represent example stereo signal waveforms andclassification of buzz drumstick hits. The buzz waveform shows acontinuous pattern of right-hand drumstick hits. The impact waveforms1200 show buzz drumstick hits incorporating multiple drumstick hits. Inparticular, impact waveform 1201 (captured by the left microphonesensor) represents a number of hits, all of which have lower peakamplitude values than corresponding values in impact waveform 1202(captured by the right microphone sensor). The impact waveforms 1200 aretherefore classified as right-hand buzz drumstick hits. As such,examples enable measurement, classification and timing of buzz hits,where drumstick hits are played as multiple hits within a singledrumstick hit at a prescribed or indeterminate speed. This enables adrummer's performance accuracy with respect to a chosen rudimentexercise and/or stick pattern incorporating buzz hits to be evaluated.

Referring to FIG. 13, there is shown a further set of example impactwaveforms 1300.

Impact waveforms 1300 represent example stereo signal waveforms andclassification of a buzz-flam drumstick hit. A buzz-flam hit waveformhas a number of left-hand drumstick hits followed by an accentedright-hand hit. Impact waveform 1301 (captured by the left microphonesensor) represents a number of hits, with several of the initial hitsbeing classified as left-hand buzz notes. Impact waveform 1302 (capturedby the right microphone sensor) also represents the same hits, with thefinal hit being classified as a right-hand accent note. In addition,impact waveforms 1301 and 1302 are collectively classified as abuzz-flam drumstick hit. As such, examples enable measurement,classification and analysis of combinations of the drumstick techniquesand/or metrics described herein, for example a buzz-flam (sometimescalled a “blam”).

Examples enable measurement and/or classification of any other drumstickhit type which exhibits unique spatial, temporal, dynamic and/orspectral characteristics.

Referring to FIG. 14, there is shown a further set of example impactwaveforms and frequency spectra 1400.

Impact waveform 1401 and frequency spectrum 1402 are for a drumstick hitthat is allowed to bounce freely away from the performance surface 206.Impact waveform 1403 and frequency spectrum 1404 are for a drumstick hitusing a technique which buries the drumstick into the performancesurface 206 for a longer contact period. As such, the stick bouncetechnique metric can be measured from the captured waveforms 1401, 1403.A drumstick hit which is allowed to bounce back freely from theperformance surface 206 has an audible difference to the performer andis identified by a shorter duration waveform and a frequency profilewith greater spectral density and fewer isolated frequency peaks.

Referring to FIG. 15, there is shown an example set of graphical userinterface screens 1500.

The example graphical user interface screens 1500 include a real-timeperformance evaluation screen 1501 and an exercise selector menu screen1502. In this example, the performance evaluation screen 1501 comprisesperformance scores 1503, music notation of the target exercise and/orthe classified performance 1504, an accuracy indicator 1505, sensorreadings 1506, a beat counter 1507, left-/right-hand indicators 1508 andan exercise descriptor 1509.

Referring to FIG. 16, there is shown another example of a system 1600.

The system 1600 is similar to the system 200 described above. However,in this example, the sensor(s) 1601 comprise one or more impact sensors.The impact sensor(s) 1601 may comprise one or more accelerometers. Anaccelerometer enables vibration measurements from within the performancesurface 1606 to be gathered. In this example, the one or more impactsensors 1601 are internal to the performance surface 1606. For example,the performance surface 1606 may be a bespoke practice pad and theimpact sensor(s) 1601 may be built into the practice pad 1606. In otherexamples, the one or more impact sensor(s) 1601 are in a retrofit form.This can allow a user to clip on or, in some other way temporarilyattach, the sensor(s) 1601 to the performance surface 1606. In otherexamples, the system 1600 embodies one or more impact sensors 1606within the impact member or members 1607. For example, one or moreimpact sensors 1606 may be embodied within the tip of a drumstick 1607or drumsticks 1607. In contrast to examples above in one or moremicrophone sensors are used for capturing acoustic data related to oneor more drumstick hits on a performance surface, in this example, one ormore accelerometers and/or one or more other vibration sensors are usedin place of, or in addition to, one or more microphones. In thisexample, the vibration profile of the performance surface can bemeasured directly, rather than the acoustic signal generated by adrumstick hit being measured.

Various measures (for example, methods, systems and computer programs)are provided to analyse a percussive performance. Such analysis mayinvolve evaluating the percussive performance. Percussive performancedata captured by one or more sensors is received. The percussiveperformance data represents one or more impact waveforms of one or morehits on one or more performance surfaces. The one or more impactwaveforms are analysed. The analysing comprises: (i) identifying one ormore characteristics of the one or more hits; (ii) classifying the oneor more hits as one or more percussive hit-types based on the one ormore characteristics; and (ii) evaluating the one or morecharacteristics and/or the one or more hit-types against performancetarget data. Performance analysis data based on the analysis is output.The performance analysis data may comprise performance evaluation data.

Various measures (for example, methods, systems and computer programs)are provided to evaluate a percussive performance. Percussiveperformance data captured by one or more sensors is received. Thepercussive performance data represents one or more impact waveforms ofone or more hits on a performance surface. The one or more impactwaveforms are analysed. The analysing comprises: (i) identifying one ormore characteristics of the one or more impact waveforms; (ii)classifying the one or more hits as one or more percussive hit-typesbased on the one or more characteristics; and (iii) evaluating the oneor more percussive hit-types against performance target data.Performance evaluation data based on the evaluating is output.

As such, more comprehensive performance evaluation can be provided thanknown systems. Known systems are not designed for, or capable of, forexample accurate timing analysis of left- and right-hand hits and/or forclassification of flams, drag, ruff, buzz or other drumstick hits. Knownsystems are not developed or optimised to evaluate dynamics withinrudiment drumstick exercises and/or to evaluate the contact time of astick with a drum pad. In contrast, advanced engineering designtechniques and signal processing methods can be used in accordance withmeasures provided herein to gather complex drumstick profiles andextract key metric data from impulse waveforms. Known systems merelymeasure and respond to the timing of an event. In contrast, measuresprovided herein provide detailed analysis and measurement of gatheredsensor data. Measures provided herein can gather high-resolution impulsewaveforms from one or more microphone sensors, which can be used toclassify a number of drumstick hit-types. Such measures can use theclassification, along with other gathered spatial, temporal, dynamicand/or spectral data to provide objective performance scores to a user.

In some examples, the one or more sensors comprise one or moremicrophones. This can allow wire-free design between a performancesurface (such as a practice pad), an impacting member (such as adrumstick) and the sensor(s), can allow a cheaper, retrofit solution,can allow the use of a standard, passive practice pad and/or performanceinstruments already on the market. However, in other examples, one ormore accelerometers (and/or one or more similar force or pressuresensors) built into the performance surface (for example a practice pad)and/or impacting member (for example drumsticks), and/or one or moreclip-on accelerometers retrofitted to a standard performance surface(for example a practice pad) may be used.

In some examples, the one or more sensors comprise a plurality ofsensors, the received percussive performance data comprises multichannelsensor data, and the classifying comprises performing positionalclassification of the one or more hits based on the multichannel sensordata. The multichannel sensor data may comprise stereo sensor data, orsensor data for more than two channels. This can allow differentiationand classification of strokes performed with the left or right hand,and/or another impact member that causes impacts in a precise andexclusive position in space and/or on the performance surface. However,in other examples, classification can be conducted if the performancesurface has different materials on different sides (for example,different spectral profiles), and/or if the performer uses differentbeaters on different sides.

In some examples, the performance evaluation data is output during thepercussive performance. This allows a real-time feedback loop whileperforming. The performer can modify their techniques and see immediateinfluence of modified technique on performance results. However, inother examples, the performance evaluation data is output after theperformance completes.

In some examples, the performance evaluation data comprises performancetechnique evaluation data representing a measure of similarity of thepercussive performance with a benchmark percussive performance. Suchexamples allow a performer to attempt to mimic the technique of anesteemed and/or respected professional and/or tutor. There are manydifferent drumming techniques used to achieve the same results, forexample many drummers hold drumsticks with the matched grip approach inwhich both sticks are held in a similar manner in each hand with thepalms facing downwards, whereas others use the traditional grip, whichuses one hand with the palm facing upwards. Neither is wrong or betterthan the other, but the performer may prefer to consider benchmarkswhich best suit their preferred style and performance ambitions.However, in other examples, an open-loop feature may be used, wheredetails of good technique are described and measured, but not comparedto specific technique benchmarks.

In some examples, the performance evaluation data comprises musicalnotation data representing the one or more percussive hit-types. Suchnotation allows the performer to compare their performance visuallyagainst target performance data (such as music notation) used during theperformance. A percussive performance can also be documented and/ortranscribed automatically, allowing time savings in accurately creatingmusic notation of drum-stick and/or other percussion patterns. However,in other examples, such data is not output.

In some examples, the one or more characteristics comprise one or morespectral metrics. As such, a wider body of data to conductclassification and analysis with can be used. However, classificationand evaluation conducted without the use of spectral metrics can stillyield valuable results.

In some examples, the performance evaluation data comprises performancetechnique evaluation data and/or performance accuracy evaluation data,and the performance technique evaluation data and/or the performanceaccuracy evaluation data is based on the one or more spectral metrics.Spectral metrics can enhance the success of the classification routineby allowing more signal features to be used in differentiating betweenhit-types and, hence, enabling more detailed evaluation. However, inother examples, spectral metrics may just be used for classification andmeasurement of parameters, and may not be used for technique and/oraccuracy evaluation.

In some examples, the one or more impact waveforms comprise a pluralityof impact waveforms of a plurality of hits, and the performanceevaluation data comprises performance technique evaluation datarepresenting spectral consistency of the plurality of hits. As such,extra technique evaluation can be conducted related to the “sound” or“timbre” (in other words, spectral profile). For example, theperformance might be accurate in terms of timing and dynamics, but theleft-hand strokes may sound different from the right-hand strokes.Spectral analysis can help identify the difference and advise on what tochange to be more consistent. However, in other examples, evaluation ofhits without incorporating spectral consistency can still yield valuableresults.

In some examples, the one or more characteristics comprise one or moretemporal metrics. Temporal metrics enable technique and performanceevaluations with respect to different time-based characteristics of thehit(s). However, in other examples, analysis of signal without temporalmetrics can still yield valuable results.

In some examples, the performance evaluation data comprises performancetechnique evaluation data and/or performance accuracy evaluation data,and the performance technique evaluation data and/or the performanceaccuracy evaluation data is based on the one or more temporal metrics.As such, evaluations can be based on temporal metrics and, hence, moredetailed feedback can be provided to the performer. However, in otherexamples, temporal metrics are used for measurement and/orclassification without evaluation taking place.

In some examples, the one or more hits correspond to one or more impactsof one or more impacting members on the performance surface, and theperformance evaluation data comprises performance technique evaluationdata representing a contact time of each of the one or more hits.Contact time, for example of a drumstick, is a valuable measure to allowtechnique related to stick bounce to be evaluated. The stick-surfacecontact time indicates how freely the performer is allowing drumsticks(controlled in the fingers of the performer) to bounce from the surface,as opposed to a more rigid and firm grip of the sticks that results in alonger contact time between the stick and the surface. The firmer gripcan cause both injury and limitations on how fast a performer can playaccurately. Temporal analysis enables a valuable technique evaluation tobe conducted. However, in other examples, technique analysis may beperformed which does not incorporate analysis of the stick contact timewith a drum pad.

In some examples, the one or more characteristics comprise one or moredynamic metrics. As such, complex dynamic patterns can be evaluated,including accents, flams and drag strokes for example. However, in otherexamples, analysis without dynamic metrics can still yield valuableresults.

In some examples, the performance evaluation data comprises performancetechnique evaluation data and/or performance accuracy evaluation data,and the performance technique evaluation data and/or the performanceaccuracy evaluation data is based on the one or more dynamic metrics.This enables complex dynamic patterns to be evaluated, includingaccents, flams and drag strokes for example. However, in other examples,evaluation without dynamic metrics can still yield valuable results.

In some examples, the one or more impact waveforms comprise a pluralityof impact waveforms of a plurality of hits, and the performanceevaluation data comprises performance technique evaluation datarepresenting dynamic consistency of the plurality of hits. This enablesdynamic constancy to be evaluated for performance and/or techniqueevaluation, for example to identify that all accent hits are similar involume, and/or that the left- and right-hand standard hits areconsistent with each other. However, in other examples, evaluationwithout dynamic metrics can still yield valuable results.

In some examples, performance context calibration is performed prior tothe analysing, and the analysing is based on calibration data resultingfrom the performance context calibration. The performance contextcalibration calibrates for a given performance context. This allowsimproved classification by calibrating, for example for a specificcontact surface, impact member, performance environment and/or any othervariable for a given performance context. However, in other examples,factory-set calibration data may be used.

In some examples, the performance context calibration comprises latencycalibration, the calibration data comprises latency calibration data,and the latency calibration comprises: (i) causing latency calibrationdata (for example, audio data) to be output via one or more transducers(for example, one or more loudspeakers); (ii) receiving, in response tothe latency calibration data being output via the one or moretransducers, data captured by the one or more sensors; and (iii)identifying a temporal latency differential based on a differencebetween a first time at which the latency calibration data is caused tobe output and a second time at which the captured data is received. Thelatency calibration data is indicative of the temporal latencydifferential. This allows latency calibration to be performed for anyembodiment including different speeds of ECU device, different types ofADC, different types of headphones, to allow for wireless communicationbetween the system components (for example, Bluetooth™ communicationsbetween the ECU and the user interface), and/or to allow Bluetooth™loudspeakers to be used with the system. However, in other examples,factory-set latency data only may be used, with a trade-off of reducedperformance for system implementations which deviates from the coresystem.

In some examples, the performance context calibration comprises dynamicscalibration, the calibration data comprises dynamics calibration data,and the dynamics calibration comprises: (i) receiving dynamicscalibration performance data captured by the one or more sensors, thedynamics calibration performance data representing at least one impactwaveform of at least one calibration hit of a first percussive hit-typeand at least one impact waveform of at least one calibration hit of asecond, different percussive hit-type; and (ii) identifying a firstdynamics threshold that enables one or more performance hits to beclassified as either the first percussive hit-type or second percussivehit-type by comparing one or more dynamic metrics based on one or moreimpact waveforms of the one or more performance hits against the firstdynamics threshold. The dynamics calibration data is indicative of thefirst dynamics threshold. This enables one or more dynamic thresholds tobe set dependent, for example, on the type of impact member and/or orperformance surface, and/or or for the specific techniques of a user.For example, an accent hit may have a different volume dependent on theperformer and their setup. A performer who hits hard on a hard, rigiddrum pad may use different threshold settings from someone who performswith less powerful hits on a rubber-coated type of drum pad. However, inother examples, factory-set dynamics data only is used, with a trade-offof reduced performance for system implementations which deviates fromthe core system.

In some examples, the dynamics calibration performance data representsat least one impact waveform of at least one calibration hit of at leastone further percussive hit-type and the dynamics calibration comprises:(i) identifying at least one further dynamics threshold that enables theone or more performance hits to be classified as the at least onefurther percussive hit-type by comparing the one or more dynamic metricsbased on the one or more impact waveforms of the one or more performancehits against the at least one further dynamics threshold. The dynamicscalibration data is indicative of the at least one further dynamicsthreshold. This allows a plurality of thresholds to be set fordistinguishing between, for example, ghost, standard and accent notes,and/or any number of granular divisions relevant to a percussiveperformance. For example, this may enable classification betweenpianissimo, piano, mezzo-piano, forte and fortissimo dynamics. However,in other examples, a single calibration threshold may be used to enableone level of classification, which can still be valuable for analysis.

In some examples, the performance context calibration comprisespercussive hit-type calibration, the calibration data comprisespercussive hit-type calibration data, and the percussive hit-typecalibration comprises: (i) receiving percussive hit-type calibrationperformance data captured by the one or more sensors, the percussivehit-type calibration performance data representing at least one impactwaveform of at least one calibration hit of at least one givenpercussive hit-type. The percussive hit-type calibration data comprisesdata derived from the at least one impact waveform of the at least onecalibration hit of the at least one given percussive hit-type. Thisallows a user to play example versions of each hit-type and enables amore bespoke classification (for example, by feature thresholds ofmachine learning) system to be implemented with respect to theparticular performer and their setup. However, in other examples,factory-set data for enabling classification can be used.

In some examples, the one or more impact waveforms comprise two or moreimpact waveforms of two or more hits respectively, and the classifyingcomprises: (i) classifying the two or more hits; and (ii) classifying,based on the classification of the two or more hits, a hit sequencecomprising the two or more hits as a different percussive hit-type frompercussive hit-types of the two or more hits. This allows complexcombinations of hits to be evaluated. For example, a single drag-tap hitsequence incorporates two consecutive ghost hits in one hand followed bya standard single note in the other hand, followed by an accent in theoriginal hand. However, in other examples, classification of single hitsonly is performed.

In some examples, the performance target data represents one or morehistoric percussive performances. This allows a user to track theirperformance progress over time and/and perform against their own priorbenchmark. However, in other examples, implementation without access tohistoric performance data can still be valuable.

In some examples, data representing the performance target data isoutput before and/or during the percussive performance. This allows theperformance target data to modify during the performance, for examplefor an exercise with a pattern that speeds up. This also enables anindicator to be provided to the user which shows which hit-type shouldbe played in which hand on the next, upcoming, beat. However, in otherexamples, performance target data is not output to the performer. Forexample, the performer may follow a performance target in a workbook.

In some examples, the one or more percussive hit-types comprise one ormore drumming hit-types. This allows analysis and evaluation for adrummer with respect to established performance target data, such as thekey drum rudiments defined by the Percussive Arts Society. However, inother examples, the one or more percussive hit-types can be of one ormore different types, for example for a xylophone and/or a glockenspiel.

In some examples, the identifying comprises: (i) calculating one or morewaveform metrics based on the one or more impact waveforms; and/or (ii)extracting one or more features from the one or more impact waveforms.As such, different characteristics and techniques for identifying thesame may be used. This provides flexibility in how the percussiveperformance is evaluated and can also provide more comprehensiveevaluation. In addition, such metrics and/or features enable highlyaccurate and robust classification and allow purely machine learntclassification. However, in other examples, one or more different typesof characteristic could be identified and/or one or more differentcharacteristic identification techniques may be used. For example, acurve matching algorithm may be used to compare waveform shapecharacteristics and apply, for example, a least squares analysis fordifferentiating between hit-types.

In some examples, the one or more characteristics are evaluated againstthe performance target data. This allows a more quantitative evaluationof the classified hit(s). For example, a hit may be classified as a flamand evaluated to be the correct hit-type in a target exercise. With thecharacteristic(s) also evaluated, flams can be evaluated for howaccurate and consistent they are as flams, to check they have the samedynamics, timing and spectral characteristics every time, etc. Inaddition, technique evaluation for, and qualitative advice forimproving, the flams can be offered. However, valuable evaluation maystill be performed without additional characteristic evaluation. Forexample, it could be checked that the correct hit-types were played inthe correct sequence, but without finer evaluation of whether thosecorrect hits were accurate in time with a reference metronome, and/orrelative to each other, or that they we consistently performed in termsof temporal, dynamic and spectral characteristics.

The above embodiments are to be understood as illustrative examples.Further embodiments are envisaged.

In examples described above, multiple sensors are used. In someexamples, only a single sensor (for example, microphone) is used.However, this may enable less accurate metrics to be gathered withrespect to the spatial classification of which hand is responsible forwhich hit.

Examples have been described above in which the system comprises asensor in a single location. In some examples, a plurality of inputsensors in different locations can be used. This can create a moreelaborate performance analysis system, for example to incorporateanalysis of a kick drum pedal and/or a user's hi-hat foot technique. Assuch, in some examples, a plurality of input sensors is used to create amore detailed performance analysis system.

In examples described above, a bespoke system is provided. In otherexamples, a host smartphone, tablet or desktop computer provides thesensing ECU and UI for capture, analysis and display of hit. Peripheraldevices for such devices may also make up some elements of the system.For example, an external stereo microphone can be connected to asmartphone handset. As such, in some examples, a smartphone and/or otherportable electronic device and/or host computer having some or all ofthe components of the system described herein is used.

Examples have been described above in which the sensor(s) comprises astereo microphone and the percussive performance data comprises one ormore acoustic profiles. However, the percussive performance data maycomprise one or more vibration profiles, for example where the sensor(s)comprises one or more accelerometers. As such, the percussiveperformance data can relate to one or more acoustic and/or vibrationcharacteristics of a percussive performance of one or more impactingmembers being used to impact and excite a performance surface. Ingeneral, the sensor(s) can comprise one or more sensors of a type otherthan a microphone. For, example the sensor(s) may comprise one or moreaccelerometers, one or more transducers and/or one or more similarsensors.

A number of different naming conventions and terminologies are used forclassifying drumstick hits in different communities and for differentmusic genres. Examples described herein can classify any type ofdrumstick hit and/or drumstick hit sequence which has unique spatial,temporal, dynamic and/or spectral characteristics, regardless of thenaming convention used.

Examples have been described above in which the percussive performanceis a drumming performance. Other of percussive performances include, butare not limited to, timpani performances, glockenspiel performances andxylophone performances.

Examples have been described above in which the performer is a drummer.However, other types of performer are envisaged depending, for example,on the nature of the percussive performance.

Examples have been described above in which the one or more hits are oneor more drumstick hits from a performer using a pair of drumsticks asimpacting members. However, the one or more hits may be of another type.Other examples of impacting members (which may be referred to as“performance implements”, “percussive devices”, or the like) include,but are not limited to, mallets, beaters (which may also be referred toas “drum beaters”), hot rods, brushes, parts of the human body (such ashuman hands), etc.. In addition, different drumsticks may be made ofdifferent drumstick materials.

Examples have been described above in which the performance surface is adrumming practice pad. Other examples of performance surfaces include,but are not limited to, drumheads, xylophones, glockenspiels, timpani,tambourines, musical instruments, table tops, cushions and parts of thehuman body (such as human legs). Other types of audible and/or vibratileperformance surface may, however, be used.

Examples have been described above in which an impact waveform asclassified as a drumstick hit-type. A hit-type could, alternatively, beidentified as, for example, a timpani, glockenspiel or xylophone hitwith a stick, beater or mallet, or tambourine hit on the palm of a hand.

The positional classification described above may be extended further toclassify the hit(s) as being from more complex typologies. Suchtypologies include, but are not limited to, left and right feet anddifferent types of impacting member.

In the context of drumstick hits, the hit-type classification algorithmmay similarly identify and classify single strokes, double strokes, dragstrokes, ruff strokes, buzz strokes, rim shots and/or other uniquelyidentifiable percussion hits and combinations of hits led by either theleft or right hand.

It is to be understood that any feature described in relation to any oneembodiment may be used alone, or in combination with other featuresdescribed, and may also be used in combination with one or more featuresof any other of the embodiments, or any combination of any other of theembodiments. Furthermore, equivalents and modifications not describedabove may also be employed without departing from the scope of theinvention, which is defined in the accompanying claims.

What is claimed is:
 1. A method of evaluating a percussive performance,the method comprising: receiving percussive performance data captured byone or more sensors, the percussive performance data representing one ormore impact waveforms of one or more hits on a performance surface;analysing the one or more impact waveforms, wherein the analysingcomprises: identifying one or more characteristics of the one or moreimpact waveforms; classifying the one or more hits as one or morepercussive hit-types based on the one or more characteristics; andevaluating the one or more percussive hit-types against performancetarget data; and outputting performance evaluation data based on saidevaluating.
 2. The method of claim 1, wherein the one or more sensorscomprise a plurality of sensors, wherein the received percussiveperformance data comprises multichannel sensor data, and wherein saidclassifying comprises performing positional classification of the one ormore hits based on the multichannel sensor data.
 3. The method of claim2, wherein the positional classification identifies whether a left orright hand of a performer was responsible for at least one given hit ofthe one or more hits.
 4. The method of claim 1, wherein the performanceevaluation data is output during the percussive performance.
 5. Themethod of claim 1, wherein the performance evaluation data comprises:performance technique evaluation data representing a measure ofsimilarity of the percussive performance with a benchmark percussiveperformance; and/or musical notation data representing the one or morepercussive hit-types.
 6. The method of claim 1, wherein the one or morecharacteristics comprise one or more spectral metrics, wherein theperformance evaluation data comprises performance technique evaluationdata and/or performance accuracy evaluation data, and wherein theperformance technique evaluation data and/or the performance accuracyevaluation data is based on the one or more spectral metrics, andwherein the one or more impact waveforms comprise a plurality of impactwaveforms of a plurality of hits, and wherein the performance evaluationdata comprises performance technique evaluation data representingspectral consistency of the plurality of hits.
 7. The method of claim 1,wherein the one or more characteristics comprise one or more temporalmetrics, wherein the performance evaluation data comprises performancetechnique evaluation data and/or performance accuracy evaluation data,and wherein the performance technique evaluation data and/or theperformance accuracy evaluation data is based on the one or moretemporal metrics.
 8. The method of claim 7, wherein the one or more hitscorrespond to one or more impacts of one or more impacting members onthe performance surface, and wherein the performance evaluation datacomprises performance technique evaluation data representing a contacttime of each of the one or more hits.
 9. The method of claim 1, whereinthe one or more characteristics comprise one or more dynamic metrics,wherein the performance evaluation data comprises performance techniqueevaluation data and/or performance accuracy evaluation data, and whereinthe performance technique evaluation data and/or the performanceaccuracy evaluation data is based on the one or more dynamic metrics.10. The method of claim 9, wherein the one or more impact waveformscomprise a plurality of impact waveforms of a plurality of hits, andwherein the performance evaluation data comprises performance techniqueevaluation data representing dynamic consistency of the plurality ofhits.
 11. The method of claim 1, comprising performing performancecontext calibration prior to said analysing, wherein said analysing isbased on calibration data resulting from said performance contextcalibration.
 12. The method of claim 11, wherein said performancecontext calibration comprises latency calibration, wherein saidcalibration data comprises latency calibration data, and wherein saidlatency calibration comprises: causing latency calibration data to beoutput via one or more transducers; receiving, in response to thelatency calibration data being output via the one or more transducers,data captured by the one or more sensors; and identifying a temporallatency differential based on a difference between a first time at whichthe latency calibration data is caused to be output and a second time atwhich the captured data is received, wherein the latency calibrationdata is indicative of the temporal latency differential.
 13. The methodof claim 11, wherein said performance context calibration comprisesdynamics calibration, wherein said calibration data comprises dynamicscalibration data, and wherein said dynamics calibration comprises:receiving dynamics calibration performance data captured by the one ormore sensors, the dynamics calibration performance data representing atleast one impact waveform of at least one calibration hit of a firstpercussive hit-type and at least one impact waveform of at least onecalibration hit of a second, different percussive hit-type; andidentifying a first dynamics threshold that enables one or moreperformance hits to be classified as either the first percussivehit-type or second percussive hit-type by comparing one or more dynamicmetrics based on one or more impact waveforms of the one or moreperformance hits against the first dynamics threshold, wherein thedynamics calibration data is indicative of the first dynamics threshold.14. The method of claim 13, wherein the dynamics calibration performancedata represents at least one impact waveform of at least one calibrationhit of at least one further percussive hit-type and wherein saiddynamics calibration comprises: identifying at least one furtherdynamics threshold that enables the one or more performance hits to beclassified as the at least one further percussive hit-type by comparingthe one or more dynamic metrics based on the one or more impactwaveforms of the one or more performance hits against the at least onefurther dynamics threshold, wherein the dynamics calibration data isindicative of the at least one further dynamics threshold.
 15. Themethod of claim 11, wherein said performance context calibrationcomprises percussive hit-type calibration, wherein said calibration datacomprises percussive hit-type calibration data, and wherein saidpercussive hit-type calibration comprises: receiving percussive hit-typecalibration performance data captured by the one or more sensors, thepercussive hit-type calibration performance data representing at leastone impact waveform of at least one calibration hit of at least onegiven percussive hit-type, wherein the percussive hit-type calibrationdata comprises data derived from the at least one impact waveform of theat least one calibration hit of the at least one given percussivehit-type.
 16. The method of claim 1, wherein the one or more impactwaveforms comprise two or more impact waveforms of two or more hitsrespectively, and wherein said classifying comprises: classifying thetwo or more hits; and classifying, based on the classification of thetwo or more hits, a hit sequence comprising the two or more hits as adifferent percussive hit-type from percussive hit-types of the two ormore hits.
 17. The method of claim 1, wherein the performance targetdata represents one or more historic percussive performances.
 18. Themethod of claim 1, wherein said identifying comprises: calculating oneor more waveform metrics based on the one or more impact waveforms;and/or extracting one or more features from the one or more impactwaveforms.
 19. A system configured to perform the method of claim
 1. 20.A computer program arranged to perform the method of claim 1.